MLOps or Machine Learning Operations is based on DevOps principles and practices that increase the efficiency of workflows and improves the quality and consistency of the machine learning solutions.
Overview of MLOps
MLOps = ML + DEV + OPS
MLOps is a Machine Learning engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops). It applies the DevOps principles and practices like continuous integration, delivery, and deployment to the machine learning process, with an aim for faster experimentation, development, and deployment of Azure machine learning models into production and quality assurance.
Here is a list of MLOps capabilities provided by Azure Machine Learning
- Create reproducible ML pipelines
- Create reusable software environments
- Register, package, and deploy models from anywhere
- Capture the governance data for the end-to-end ML lifecycle
- Notify and alert on events in the ML lifecycle
- Monitor ML applications for operational and ML-related issues
- Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines
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